mamba create -n bench -c anaconda -c conda-forge ipython numpy "libblas=*=*mkl" "liblapack=*=*mkl" intel-openmp

• =*=*openblas: uses OpenBLAS; default
• =*=*mkl: uses Intel’s Math Kernel Library (MKL)
• =*=*accelerate: uses Apple’s Accelerate framework with vecLib
• =*=*blis: uses AMD' AOCL-BLIS

TODO:

• how about MKL/Accelerate builds for scipy and sklearn?
• does intel have optimizations or hardware acceleration for Pandas

Matrix Multiplication (2048x2048) on Apple M1 (4+4)

• OpenBLAS: 94.7 ms ± 2.34 ms per loop
• Accelerate: 68.6 ms ± 321 µs per loop
• Notes: OpenBLAS uses all available cores, while Accelerate only dedicates one performance core. Accelerate still ends up better with less CPU usage.

Matrix Multiplication (2048x2048) on Mac Intel i7 (6)

• OpenBLAS: 96.5 ms ± 6.72 ms per loop
• MKL: 74.6 ms ± 3.35 ms per loop
• Accelerate: 99 ms ± 4.18 ms per loop
• Notes: MKL only uses the physical cores, while OpenBLAS and Accelerate includes the hyperthreads. All libraries consumed ~100W peak while performing the operation.

on AMD, MKL is better than BLIS